R Tutorial

An introduction to R

Introduction

This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .

R Project Logo

Download

Download at https://www.r-project.org/
Download RStudio at https://rstudio.com/products/rstudio/download/

Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3

## [1] 8

# 24 / (1 + 2) = 8
24 / (1 + 2)

## [1] 8

# 2 * 2 * 2 = 8
2^3

## [1] 8

# 8 * 8 = 64
sqrt(64)

## [1] 8

# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000)

## [1] 8

Functions

has many useful built in functions

1:10

##  [1]  1  2  3  4  5  6  7  8  9 10

as.character(1:10)

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

rep(1:2, times = 5)

##  [1] 1 2 1 2 1 2 1 2 1 2

rep(1:5, times = 2)

##  [1] 1 2 3 4 5 1 2 3 4 5

rep(1:5, each = 2)

##  [1] 1 1 2 2 3 3 4 4 5 5

rep(1:5, length.out = 7)

## [1] 1 2 3 4 5 1 2

seq(5, 50, by = 5)

##  [1]  5 10 15 20 25 30 35 40 45 50

seq(5, 50, length.out = 5)

## [1]  5.00 16.25 27.50 38.75 50.00

paste(1:10, 20:30, sep = "-")

##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"

paste(1:10, collapse = "-")

## [1] "1-2-3-4-5-6-7-8-9-10"

paste0("x", 1:10)

##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"

min(1:10)

## [1] 1

max(1:10)

## [1] 10

range(1:10)

## [1]  1 10

mean(1:10)

## [1] 5.5

sd(1:10)

## [1] 3.02765

Custom Functions

Users can also create their own functions

customFunction1 <- function(x, y) {
  z <- 100 * x / (x + y)
  paste(z, "%")
}
customFunction1(x = 10, y = 90)

## [1] "10 %"

customFunction2 <- function(x) {
  mymin <- mean(x - sd(x))
  mymax <- mean(x) + sd(x)
  print(paste("Min =", mymin))
  print(paste("Max =", mymax))
}
customFunction2(x = 1:10)

## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"

`for` loops and `if` `else` statements

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx

##  [1]  3  6  9 12 15 18 21 24 27 30

xx %% 2 #gives the remainder when divided by 2

##  [1] 1 0 1 0 1 0 1 0 1 0

for(i in 1:length(xx)) {
  if((xx[i] %% 2) == 0) {
    print(paste(xx[i],"is Even"))
  } else { 
      print(paste(xx[i],"is Odd")) 
    }
}

## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"

# or
ifelse(xx %% 2 == 0, "Even", "Odd")

##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"

paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))

##  [1] "3 is Odd"   "6 is Even"  "9 is Odd"   "12 is Even" "15 is Odd"  "18 is Even" "21 is Odd"  "24 is Even" "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

c(): a string of values
matrix(): a two dimensional matrix in one format
data.frame(): a two dimensional matrix where each column can be a different format
list():

A string…

xc <- 1:10
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

xc <- c(1,2,3,4,5,6,7,8,9,10)
xc

##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1    2    3    4    5    6    7    8    9    10
##  [2,]   11   12   13   14   15   16   17   18   19    20
##  [3,]   21   22   23   24   25   26   27   28   29    30
##  [4,]   31   32   33   34   35   36   37   38   39    40
##  [5,]   41   42   43   44   45   46   47   48   49    50
##  [6,]   51   52   53   54   55   56   57   58   59    60
##  [7,]   61   62   63   64   65   66   67   68   69    70
##  [8,]   71   72   73   74   75   76   77   78   79    80
##  [9,]   81   82   83   84   85   86   87   88   89    90
## [10,]   91   92   93   94   95   96   97   98   99   100

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

A data frame…

xd <- data.frame(
  x1 = c("aa","bb","cc","dd","ee",
         "ff","gg","hh","ii","jj"),
  x2 = 1:10,
  x3 = c(1,1,1,1,1,2,2,2,3,3),
  x4 = rep(c(1,2), times = 5),
  x5 = rep(1:5, times = 2),
  x6 = rep(1:5, each = 2),
  x7 = seq(5, 50, by = 5),
  x8 = log10(1:10),
  x9 = (1:10)^3,
  x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

A list…

xl <- list(xc, xm, xd)
xl[[1]]

##  [1]  1  2  3  4  5  6  7  8  9 10

xl[[2]]

##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]    1   11   21   31   41   51   61   71   81    91
##  [2,]    2   12   22   32   42   52   62   72   82    92
##  [3,]    3   13   23   33   43   53   63   73   83    93
##  [4,]    4   14   24   34   44   54   64   74   84    94
##  [5,]    5   15   25   35   45   55   65   75   85    95
##  [6,]    6   16   26   36   46   56   66   76   86    96
##  [7,]    7   17   27   37   47   57   67   77   87    97
##  [8,]    8   18   28   38   48   58   68   78   88    98
##  [9,]    9   19   29   39   49   59   69   79   89    99
## [10,]   10   20   30   40   50   60   70   80   90   100

xl[[3]]

##    x1 x2 x3 x4 x5 x6 x7        x8   x9   x10
## 1  aa  1  1  1  1  1  5 0.0000000    1  TRUE
## 2  bb  2  1  2  2  1 10 0.3010300    8  TRUE
## 3  cc  3  1  1  3  2 15 0.4771213   27  TRUE
## 4  dd  4  1  2  4  2 20 0.6020600   64 FALSE
## 5  ee  5  1  1  5  3 25 0.6989700  125 FALSE
## 6  ff  6  2  2  1  3 30 0.7781513  216  TRUE
## 7  gg  7  2  1  2  4 35 0.8450980  343  TRUE
## 8  hh  8  2  2  3  4 40 0.9030900  512 FALSE
## 9  ii  9  3  1  4  5 45 0.9542425  729 FALSE
## 10 jj 10  3  2  5  5 50 1.0000000 1000 FALSE

Selecting Data

xc[5] # 5th element in xc

## [1] 5

xd$x3[5] # 5th element in col "x3"

## [1] 1

xd[5,"x3"] # row 5, col "x3"

## [1] 1

xd$x3 # all of col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[,"x3"] # all rows, col "x3"

##  [1] 1 1 1 1 1 2 2 2 3 3

xd[3,] # row 3, all cols

##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE

xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"

##   x4 x5
## 2  2  2
## 4  2  4

xl[[3]]$x1 # 3rd object in the list, col "x1

##  [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"

regexpr

xx <- data.frame(Name = c("Item 1 (detail 1)",
                          "Item 20 (detail 20)",
                          "Item 300 (detail 300)"),
                 Item = NA,
                 Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx

##                    Name     Item     Detail
## 1     Item 1 (detail 1)   Item 1   detail 1
## 2   Item 20 (detail 20)  Item 20  detail 20
## 3 Item 300 (detail 300) Item 300 detail 300

Data Formats

Data can also be saved in many formats:

numeric
integer
character
factor
logical

xd$x3 <- as.character(xd$x3)
xd$x3

##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"

xd$x3 <- as.numeric(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3

xd$x3 <- as.factor(xd$x3)
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3

xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3

##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1

xd$x10

##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE

as.numeric(xd$x10) # TRUE = 1, FALSE = 0

##  [1] 1 1 1 0 0 1 1 0 0 0

sum(xd$x10)

## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()

##  num [1:10] 1 2 3 4 5 6 7 8 9 10

str(xm) # matrix()

##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...

str(xd) # data.frame()

## 'data.frame':    10 obs. of  10 variables:
##  $ x1 : chr  "aa" "bb" "cc" "dd" ...
##  $ x2 : int  1 2 3 4 5 6 7 8 9 10
##  $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
##  $ x4 : num  1 2 1 2 1 2 1 2 1 2
##  $ x5 : int  1 2 3 4 5 1 2 3 4 5
##  $ x6 : int  1 1 2 2 3 3 4 4 5 5
##  $ x7 : num  5 10 15 20 25 30 35 40 45 50
##  $ x8 : num  0 0.301 0.477 0.602 0.699 ...
##  $ x9 : num  1 8 27 64 125 216 343 512 729 1000
##  $ x10: logi  TRUE TRUE TRUE FALSE FALSE TRUE ...

str(xl) # list()

## List of 3
##  $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
##  $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
##  $ :'data.frame':    10 obs. of  10 variables:
##   ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
##   ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
##   ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
##   ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
##   ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
##   ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
##   ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
##   ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
##   ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
##   ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...

Packages

Additional libraries can be installed and loaded for use.

install.packages("scales")

library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx

##    Values  Rescaled
## 1       1  1.000000
## 2       2  4.222222
## 3       3  7.444444
## 4       4 10.666667
## 5       5 13.888889
## 6       6 17.111111
## 7       7 20.333333
## 8       8 23.555556
## 9       9 26.777778
## 10     10 30.000000

libraries can also be used without having to load them

scales::rescale(1:10, to = c(1,30))

##  [1]  1.000000  4.222222  7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000

Data Wrangling

R for Data Science - https://r4ds.had.co.nz/

xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
                 Data1 = 1:10, 
                 Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000

xx$Data1 < 5 # which are less than 5

##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE

xx[xx$Data1 < 5,]

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx[xx$Group == "X", c("Group","Data2","NewData1")]

##   Group Data2 NewData1
## 1     X    10       11
## 2     X    20       22
## 6     X    60       66
## 7     X    70       77
## 8     X    80       88

Data wrangling with tidyverse and pipes (%>%)

library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
  mutate(Data1 = 1:10, 
         Data2 = seq(10, 100, by = 10),
         NewData1 = Data1 + Data2,
         NewData2 = Data1 * 1000)
xx

##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      Y     6    60       66     6000
## 7      Y     7    70       77     7000
## 8      X     8    80       88     8000
## 9      X     9    90       99     9000
## 10     X    10   100      110    10000

filter(xx, Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Data1 < 5)

##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000

xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)

##   Group NewColName NewData1
## 1     X         10       11
## 2     X         20       22
## 3     X         80       88
## 4     X         90       99
## 5     X        100      110

xs <- xx %>% 
  group_by(Group) %>% 
  summarise(Data2_mean = mean(Data2),
            Data2_sd = sd(Data2),
            NewData2_mean = mean(NewData2),
            NewData2_sd = sd(NewData2))
xs

## # A tibble: 2 × 5
##   Group Data2_mean Data2_sd NewData2_mean NewData2_sd
##   <chr>      <dbl>    <dbl>         <dbl>       <dbl>
## 1 X             60     41.8          6000       4183.
## 2 Y             50     15.8          5000       1581.

xx %>% left_join(xs, by = "Group")

##    Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1      X     1    10       11     1000         60 41.83300          6000    4183.300
## 2      X     2    20       22     2000         60 41.83300          6000    4183.300
## 3      Y     3    30       33     3000         50 15.81139          5000    1581.139
## 4      Y     4    40       44     4000         50 15.81139          5000    1581.139
## 5      Y     5    50       55     5000         50 15.81139          5000    1581.139
## 6      Y     6    60       66     6000         50 15.81139          5000    1581.139
## 7      Y     7    70       77     7000         50 15.81139          5000    1581.139
## 8      X     8    80       88     8000         60 41.83300          6000    4183.300
## 9      X     9    90       99     9000         60 41.83300          6000    4183.300
## 10     X    10   100      110    10000         60 41.83300          6000    4183.300

Read/Write data

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

For excel sheets, the package readxl can be used to read in sheets of data.

library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")

Tidy Data

Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html

yy <- xx %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = round(mean(DTF),1)) %>% 
  arrange(Location)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          Location            Mean_DTF
##   <chr>         <chr>                  <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh     86.7
## 2 ILL 618 AGL   Jessore, Bangladesh     79.3
## 3 Laird AGL     Jessore, Bangladesh     76.8
## 4 CDC Maxim AGL Metaponto, Italy       134. 
## 5 ILL 618 AGL   Metaponto, Italy       138. 
## 6 Laird AGL     Metaponto, Italy       137. 
## 7 CDC Maxim AGL Saskatoon, Canada       52.5
## 8 ILL 618 AGL   Saskatoon, Canada       47  
## 9 Laird AGL     Saskatoon, Canada       56.8

yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
##   <chr>                         <dbl>              <dbl>               <dbl>
## 1 CDC Maxim AGL                  86.7               134.                52.5
## 2 ILL 618 AGL                    79.3               138.                47  
## 3 Laird AGL                      76.8               137.                56.8

yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy

## # A tibble: 9 × 3
## # Groups:   Name [3]
##   Name          TraitName           Value
##   <chr>         <chr>               <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh  86.7
## 2 ILL 618 AGL   Jessore, Bangladesh  79.3
## 3 Laird AGL     Jessore, Bangladesh  76.8
## 4 CDC Maxim AGL Metaponto, Italy    134. 
## 5 ILL 618 AGL   Metaponto, Italy    138. 
## 6 Laird AGL     Metaponto, Italy    137. 
## 7 CDC Maxim AGL Saskatoon, Canada    52.5
## 8 ILL 618 AGL   Saskatoon, Canada    47  
## 9 Laird AGL     Saskatoon, Canada    56.8

yy <- yy %>% spread(key = Name, value = Value)
yy

## # A tibble: 3 × 4
##   TraitName           `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
##   <chr>                         <dbl>         <dbl>       <dbl>
## 1 Jessore, Bangladesh            86.7          79.3        76.8
## 2 Metaponto, Italy              134.          138.        137. 
## 3 Saskatoon, Canada              52.5          47          56.8

Base Plotting

We will start with some basic plotting using the base function plot()

Tutorial 1 - http://www.sthda.com/english/wiki/r-base-graphs
Tutorial 2 - https://bookdown.org/rdpeng/exdata/the-base-plotting-system-1.html

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

Now lets create some random and normally distributed data to make some more complicated plots

# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru

##   [1] 62.538869 70.283159 81.568792 50.656633 72.526105 31.194347 93.067010 71.093854 97.133464 42.744200 51.767055 59.916275 14.799573 53.881371 38.686017
##  [16] 12.497996 32.932248 17.540242 43.366423  9.049412 87.757078 71.897624 19.339576 26.797837 91.652205 62.239372 11.428901 51.428297 79.328071 70.447445
##  [31] 98.144861 18.800969 36.039387 37.739478 83.083636 86.979048 30.266637 30.223365 20.537108 99.036934 51.061048 97.340244  7.548624 11.312006 90.716101
##  [46] 33.919443 22.797261 32.414426 27.642306 56.165285 43.346536 91.765050 21.890525 89.291760 64.493569 21.364677 48.438501 18.339559 40.023248 63.961369
##  [61] 14.261351 62.323622 50.274616 50.298866 23.629595 27.104261 50.323037 18.925387 59.572082 15.959614 89.239017 38.266332 52.980272 82.546570 43.985057
##  [76] 96.931228 87.321687 96.408854 26.171772 67.772228 88.392424 65.292211 17.078672 23.210631 73.805187 76.350819 38.192138 16.877276 86.934455 25.321733
##  [91]  5.921609 29.726907 92.214206 28.467879 96.038433 69.318168 20.771167 72.521903 81.482403 88.590246

plot(x = ru)

order(ru)

##   [1]  91  43  20  44  27  16  61  13  70  88  83  18  58  32  68  23  39  97  56  53  47  84  65  90  79  24  66  49  94  92  38  37   6  48  17  46  33  34
##  [39]  87  72  15  59  10  51  19  75  57  63  64  67   4  41  28  11  73  14  50  69  12  26  62   1  60  55  82  80  96   2  30   8  22  98   5  85  86  29
##  [77]  99   3  74  35  89  36  77  21  81 100  71  54  45  25  52  93   7  95  78  76   9  42  31  40

ru<- ru[order(ru)]
ru

##   [1]  5.921609  7.548624  9.049412 11.312006 11.428901 12.497996 14.261351 14.799573 15.959614 16.877276 17.078672 17.540242 18.339559 18.800969 18.925387
##  [16] 19.339576 20.537108 20.771167 21.364677 21.890525 22.797261 23.210631 23.629595 25.321733 26.171772 26.797837 27.104261 27.642306 28.467879 29.726907
##  [31] 30.223365 30.266637 31.194347 32.414426 32.932248 33.919443 36.039387 37.739478 38.192138 38.266332 38.686017 40.023248 42.744200 43.346536 43.366423
##  [46] 43.985057 48.438501 50.274616 50.298866 50.323037 50.656633 51.061048 51.428297 51.767055 52.980272 53.881371 56.165285 59.572082 59.916275 62.239372
##  [61] 62.323622 62.538869 63.961369 64.493569 65.292211 67.772228 69.318168 70.283159 70.447445 71.093854 71.897624 72.521903 72.526105 73.805187 76.350819
##  [76] 79.328071 81.482403 81.568792 82.546570 83.083636 86.934455 86.979048 87.321687 87.757078 88.392424 88.590246 89.239017 89.291760 90.716101 91.652205
##  [91] 91.765050 92.214206 93.067010 96.038433 96.408854 96.931228 97.133464 97.340244 98.144861 99.036934

plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd

##   [1] 28.77238 51.26599 41.95373 50.89091 55.40515 57.02875 58.34836 40.88887 42.97132 57.19720 59.07331 59.60845 46.40600 50.04201 29.93391 64.88161 54.03598
##  [18] 50.15501 54.37263 55.12246 47.71096 64.85229 41.66809 58.00431 42.86460 49.57096 48.25181 33.91409 40.37045 42.54715 47.24055 63.19855 36.96430 72.30855
##  [35] 59.51015 44.63597 55.98341 37.61457 75.28938 53.28429 45.88430 66.45688 38.58680 56.60456 49.18184 52.32662 51.50898 61.53160 59.77326 52.68717 50.47757
##  [52] 40.50437 64.43271 32.98335 38.72445 48.85593 40.25366 41.01280 34.72524 43.10590 51.97198 60.08360 47.62868 47.29358 50.40605 64.07392 50.97325 53.35261
##  [69] 54.91854 57.23007 58.97665 22.47198 62.64252 51.44093 52.27244 56.00558 48.29553 50.55070 79.06036 34.49398 48.49014 63.16650 56.08458 59.27560 58.68439
##  [86] 46.94129 56.89308 42.76817 62.20402 42.77536 55.24721 55.82392 59.75372 63.72599 66.09482 47.99536 47.82059 59.35653 54.00985 39.26110

nd <- nd[order(nd)]
nd

##   [1] 22.47198 28.77238 29.93391 32.98335 33.91409 34.49398 34.72524 36.96430 37.61457 38.58680 38.72445 39.26110 40.25366 40.37045 40.50437 40.88887 41.01280
##  [18] 41.66809 41.95373 42.54715 42.76817 42.77536 42.86460 42.97132 43.10590 44.63597 45.88430 46.40600 46.94129 47.24055 47.29358 47.62868 47.71096 47.82059
##  [35] 47.99536 48.25181 48.29553 48.49014 48.85593 49.18184 49.57096 50.04201 50.15501 50.40605 50.47757 50.55070 50.89091 50.97325 51.26599 51.44093 51.50898
##  [52] 51.97198 52.27244 52.32662 52.68717 53.28429 53.35261 54.00985 54.03598 54.37263 54.91854 55.12246 55.24721 55.40515 55.82392 55.98341 56.00558 56.08458
##  [69] 56.60456 56.89308 57.02875 57.19720 57.23007 58.00431 58.34836 58.68439 58.97665 59.07331 59.27560 59.35653 59.51015 59.60845 59.75372 59.77326 60.08360
##  [86] 61.53160 62.20402 62.64252 63.16650 63.19855 63.72599 64.07392 64.43271 64.85229 64.88161 66.09482 66.45688 72.30855 75.28938 79.06036

plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

ggplot2

Lets be honest, the base plots are ugly! The ggplot2 package gives the user to create a better, more visually appealing plots. Additional packages such as ggbeeswarm and ggrepel also contain useful functions to add to the functionality of ggplot2.

ggplot2 - https://ggplot2.tidyverse.org/
Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
                          rnorm(50, mean = 60, sd = 5)),
                 group = factor(rep(1:2, each = 50)),
                 label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

mp2 <- mp + geom_violin() + 
  geom_boxplot(width = 0.1, fill = "white") +
  geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)

library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
          common.legend = T, legend = "bottom")

Statistics

Handbook of Biological Statistics - http://biostathandbook.com/
R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html

# Prep data
lev_Loc  <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
  mutate(Location = factor(Location, levels = lev_Loc),
         Name = factor(Name, levels = lev_Name))
xx <- dd %>%
  group_by(Name, Location) %>%
  summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)

## # A tibble: 3 × 4
## # Groups:   Name [3]
##   Name          `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
##   <fct>                       <dbl>                 <dbl>              <dbl>
## 1 ILL 618 AGL                  47                    79.3               138.
## 2 CDC Maxim AGL                52.5                  86.7               134.
## 3 Laird AGL                    56.8                  76.8               137.

# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
  geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF, 
                      color = Name, group = Name, shape = Name)) +
  geom_point(size = 2.5, alpha = 0.7) + 
  geom_line(size = 1, alpha = 0.7) +
  theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")

From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.

summary(aov(DTF ~ Name * Location, data = dd))

##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32931 2598.336  < 2e-16 ***
## Name:Location  4    560     140   11.044 2.52e-06 ***
## Residuals     45    570      13                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.

If we only have two things to compare, we could do a t-test.

xx <- dd %>% 
  filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
  spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -32.18265 -25.48402
## sample estimates:
## mean of x mean of y 
##  52.11111  80.94444

DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.

xx <- dd %>% 
  filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
         Location == "Metaponto, Italy") %>%
  spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)

## 
##  Welch Two Sample t-test
## 
## data:  xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.059739  7.059739
## sample estimates:
## mean of x mean of y 
##  137.8333  136.8333

DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.

pch Plot

xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
                 y = rep(5:1, each = 6, length.out = 26),
                 pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
  geom_point(color = "darkred", fill = "darkblue", size = 5) +
  geom_text(aes(label = pch), nudge_x = -0.25) +
  scale_shape_manual(values = xx$pch) +
  scale_x_continuous(breaks = 6:1) +
  scale_y_continuous(breaks = 6:1) +
  theme_void() +
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5),
        plot.subtitle = element_text(hjust = 0.5),
        axis.text = element_blank(),
        axis.ticks = element_blank()) +
  labs(title = "Plot symbols in R (pch)",
       subtitle = "color = \"darkred\", fill = \"darkblue\"",
       x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")

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